Evolution of neural control structures: Some experiments on mobile robots

From perception to action and form action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakness of incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and discussed. The results show that the combination of genetic algorithms and neural networks is a very interesting technique for the development of control structures in autonomous agents. The time necessary for evolution, on the other hand, is very important limitation of the evolutionary approach

Evolution of Homing Navigation in a Real Mobile Robot

In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We show that the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development of an internal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy

Evolutionary Neurocontrollers for Autonomous Mobile Robots

In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent work in the attempt to provide a unified picture within which the reader can compare the effects of systematic variations on the experimental settings. After describing some key principles for building mobile robots and tools suitable for experiments in adaptive robotics, we give an overview of different approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that different environments can shape very different behaviors and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two different experiments from the perspective of changing environments and robot morphologies. Finally, we investigate the possibility of evolving plastic neurocontrollers and analyze and evolved neurocontroller that relies on fast and continuously changes synapses characterized by dynamic stability. We conclude by reviewing the implications of this methodology for engineering, biology, cognitive science, and artificial life, and point at future directions of research

Active Perception, Navigation, Homing, and Grasping: An autonomous Perspective

Perception is needed for action, not for the pure sake of the construction of abstract representations, although it does not exclude the role of internal representations for mediating complex behaviors. We think that, for the purpose of building autonomous robots, active perception requires specific recipes for three related aspects: the design of the physical sensory system, the modality and type of information extracted, and the structure and functioning of the control system. We outline a set of solutions for these three aspects and describe their implementation on a real mobile robot through a set of three different experiments using a combination of neural networks and genetic algorithms. The results show that active perception is a useful feature that is exploited by autonomous agents. The experiments show that the combination of genetic algorithms and neural networks is a feasible and fruitful technique for the development of active perception in autonomous agents

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify and analyse a prominent example of adaptive system: robot swarms equipped with obstacle-avoidance self-assembly strategies. The analysis exploits the statistical model checker PVesta

Evolution of Embodied Intelligence

We provide an overview of the evolutionary approach to the emergence of artificial intelligence in embodied behavioral agents. This approach, also known as Evolutionary Robotics, builds and capitalizes upon the interactions between the embodied agent and its environment. Although we cover research carried out in several laboratories around the world, the choice of topics and approaches is based on work carried out at EPFL. We describe a large number of experiments including evolution of single robots in environments of increasing complexity, competitive and cooperative evolution, evolution of vi-sion-based systems, evolution of learning, and evolution of electronics and morphologies for autonomous robot

Elevated hepatocyte paraffin 1 and neprilysin expression in hepatocellular carcinoma are correlated with longer survival.

Hepatocyte paraffin 1 (Hep Par 1) and neprilysin (CD10) are well-known markers of hepatocellular carcinoma (HCC). To assess their potential prognostic role, we conducted a retrospective analysis of 97 formalin-fixed and paraffin-embedded HCC from patients treated by surgery with curative intent, using standard immunohistochemical procedures and semiquantitative analysis. Strong Hep Par 1 expression and canalicular CD10 staining pattern were significantly correlated with smaller tumor size (p=0.007 and 0.04, respectively). On univariate analysis, longer overall survival was observed in patients with strong Hep Par 1 expression (p=0.0005) and in patients with a CD10can staining pattern (p=0.02). On multivariate analysis, the combined immunohistochemical score (CIS) obtained by addition of Hep Par 1 and CD10can scores and subtraction of cytoplasmic CD10 score was retained as the single most important prognostic factor (p=0.001). Patients with a CIS <4 had a 3.5-fold increased risk of death, as compared to those with a CIS >or=4. In conclusion, strong Hep Par 1 expression, presence of CD10can labeling, and absence of CD10cyt staining are favorable prognostic factors in HCC, which can be easily combined into a single immunohistochemical score for routine clinical use